Celebrating the impact of IDSS
A two-day conference at MIT reflected on the impact of the Institute for Data, Systems, and Society since its launch, as founding Director Munther Dahleh prepares to step down.
A two-day conference at MIT reflected on the impact of the Institute for Data, Systems, and Society since its launch, as founding Director Munther Dahleh prepares to step down.
Statistics tools support the idea that all radio bursts may repeat if observed long enough.
Senior Ananya Gurumurthy adds her musical talents to her math and computer science studies to advocate using data for social change.
Fifteen principal investigators from across MIT will conduct early work to solve issues ranging from water contamination to aquaculture monitoring and management.
A new machine-learning model makes more accurate predictions about ocean currents, which could help with tracking plastic pollution and oil spills, and aid in search and rescue.
Leo Anthony Celi invites industry to broaden its focus in gathering and analyzing clinical data for every population.
Models trained using common data-collection techniques judge rule violations more harshly than humans would, researchers report.
Citadel founder and CEO Ken Griffin visits MIT, discusses how technology will continue to transform trading and investing.
The system they developed eliminates a source of bias in simulations, leading to improved algorithms that can boost the performance of applications.
A collaborative research team from the MIT-Takeda Program combined physics and machine learning to characterize rough particle surfaces in pharmaceutical pills and powders.
Senior Amelia Dogan brings together computer science, city planning, and American studies to work for social change.
Widely recognized leader in statistics and machine learning to succeed Munther Dahleh.
MIT ReACT and Innovation Leadership Bootcamp provide valuable opportunities.
MIT researchers exhibit a new advancement in autonomous drone navigation, using brain-inspired liquid neural networks that excel in out-of-distribution scenarios.
With the right building blocks, machine-learning models can more accurately perform tasks like fraud detection or spam filtering.